Add LTPDADatabaseConnectionManager implementation. Java code
line source
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%
% Compute log-likelihood for SSM objects
%
% M Nofrarias 15-06-09
%
% $Id: loglikelihood_ssm.m,v 1.3 2011/11/16 08:52:49 nikos Exp $
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [loglk snr]= loglikelihood_ssm(xn,in,out,noise,model,params,inNames,outNames, spl, Amats, Bmats, Cmats, Dmats)
loglk = 0;
snr = 0;
switch class(in)
case 'ao'
% parameters
fs = noise(1).fs;
N = length(noise(1).y);
if (numel(in) == 1 && numel(out) == 1)
xn = double(xn);
spl = plist('set', 'for bode', ...
'outputs', outNames, ...
'inputs', inNames, ...
'reorganize', false,...
'f', in(1).x);
eval = copy(model,1);
% set parameter values
eval.doSetParameters(params, xn);
% make numeric
eval.doSubsParameters(params, true);
% do bode
h11 = bode(eval, spl, 'internal');
f = in.x;
% spectra to variance
C11 = (N*fs/2)*noise(1).y;
% compute elements of inverse cross-spectrum matrix
InvS11 = 1./C11;
% compute log-likelihood terms first, all at once does not cancel the
% imag part when multiplying x.*conj(x)
v1v1 = conj(out(1).y - h11.y.*in(1).y).*(out(1).y - h11.y.*in(1).y);
tmplt = h11.*in(1).y;
%computing SNR
snrexp = utils.math.stnr(tmplt,0,out(1).y,0,InvS11,0,0,0);
snr = snr + 20*log10(snrexp);
log1exp = sum(InvS11.*v1v1);
loglk = loglk + log1exp;
elseif (numel(in) == 2 && numel(out) == 2)
f = in(1).x;
xn = double(xn);
spl = plist('set', 'for bode', ...
'outputs', outNames, ...
'inputs', inNames, ...
'reorganize', false,...
'f', in(1).x);
eval = copy(model,1);
% set parameter values
eval.doSetParameters(params, xn);
% make numeric
eval.doSubsParameters(params);
% do bode
h = bode(eval, spl, 'internal');
h11 = h(1);
h12 = h(2);
h21 = h(3);
h22 = h(4);
% spectra to variance
C11 = (N*fs/2)*noise(1).y;
C22 = (N*fs/2)*noise(2).y;
C12 = (N*fs/2)*noise(3).y;
C21 = (N*fs/2)*noise(4).y;
% compute elements of inverse cross-spectrum matrix
InvS11 = (C22./(C11.*C22 - C12.*C21));
InvS22 = (C11./(C11.*C22 - C12.*C21));
InvS12 = (C21./(C11.*C22 - C12.*C21));
InvS21 = (C12./(C11.*C22 - C12.*C21));
% compute log-likelihood terms first, all at once does not cancel the
% imag part when multiplying x.*conj(x)
v1v1 = conj(out(1).y - h11.y.*in(1).y - h12.y.*in(2).y).*(out(1).y - h11.y.*in(1).y - h12.y.*in(2).y);
v2v2 = conj(out(2).y - h21.y.*in(1).y - h22.y.*in(2).y).*(out(2).y - h21.y.*in(1).y - h22.y.*in(2).y);
v1v2 = conj(out(1).y - h11.y.*in(1).y - h12.y.*in(2).y).*(out(2).y - h21.y.*in(1).y - h22.y.*in(2).y);
v2v1 = conj(out(2).y - h21.y.*in(1).y - h22.y.*in(2).y).*(out(1).y - h11.y.*in(1).y - h12.y.*in(2).y);
tmplt1 = h11.*in(1).y + h12.*in(2).y;
tmplt2 = h21.*in(1).y + h22.*in(2).y;
%computing SNR
snrexp = utils.math.stnr(tmplt1,tmplt2,out(1).y,out(2).y,InvS11,InvS22,InvS12,InvS21);
snr = snr + 20*log10(snrexp);
log1exp = sum(InvS11.*v1v1 + InvS22.*v2v2 - InvS12.*v1v2 - InvS21.*v2v1);
loglk = loglk + log1exp;
else
error('This method is only implemented for 1 input / 1 output model or for 2 inputs / 2 outputs models');
end
case 'matrix'
% parameters
fs = in(1).objs(1).fs;
% num. experiments
nexp = numel(in);
noutChannels = numel(out(1).objs);
N = length(noise(1).objs(1).y);
loglk = 0;
for nnn = 1:nexp
if ((numel(in(1).objs) == 1) && numel(out(1).objs) == 1)
freqs = in(nnn).objs(1).data.getX;
xn = double(xn);
spl = plist('set', 'for bode', ...
'outputs', outNames, ...
'inputs', inNames, ...
'reorganize', false,...
'f', freqs);
eval = copy(model(nnn),1);
% set parameter values
eval.doSetParameters(params, xn);
% make numeric
eval.doSubsParameters(params, true);
% do bode
h(:,1) = bode(eval, spl, 'internal');
for j = 1:noutChannels^2
% spectra to variance
% (N*fs/2)* this multiplication is done now in mcmc
C(:,j) = noise(nnn).objs(j).data.getY;
end
% compute elements of inverse cross-spectrum matrix
InvS11 = 1./C(:,1);
% compute log-likelihood terms first, all at once does not cancel the
% imag part when multiplying x.*conj(x)
in1 = in(nnn).objs(1).data.getY;
out1 = out(nnn).objs(1).data.getY;
% matrix index convention: H(1,1)->h(1) H(2,1)->h(2) H(1,2)->h(3) H(2,2)->h(4)
v1v1 = conj(out1 - h.getObjectAtIndex(1,1).y.*in1).*(out1 - h.getObjectAtIndex(1,1).y.*in1);
tmplt = h.getObjectAtIndex(1,1).y.*in1;
%computing SNR
snrexp = utils.math.stnr(tmplt,0,out1,0,InvS11,0,0,0);
snr = snr + 20*log10(snrexp);
log1exp = sum(InvS11.*v1v1);
loglk = loglk + log1exp;
elseif ((numel(in(1).objs) == 2) && numel(out(1).objs) == 2)
freqs = in(nnn).objs(1).data.getX;
xn = double(xn);
spl.pset('f', freqs);
eval = model(nnn);
eval.setA(Amats);
eval.setB(Bmats);
eval.setC(Cmats);
eval.setD(Dmats);
% set parameter values
eval.doSetParameters(params, xn);
% make numeric
eval.doSubsParameters(params, true);
% do bode
[h1 h2 h3 h4] = bode(eval, spl);
for j = 1:noutChannels^2
% spectra to variance
% (N*fs/2)* this multiplication is done now in mcmc
C(:,j) = noise(nnn).objs(j).data.getY;
end
% compute elements of inverse cross-spectrum matrix
detm = (C(:,1).*C(:,4) - C(:,2).*C(:,3));
InvS11 = C(:,4)./detm; %1 4
InvS22 = C(:,1)./detm; %4 1
InvS12 = C(:,2)./detm; %2 2
InvS21 = C(:,3)./detm; %3 3
% compute log-likelihood terms first, all at once does not cancel the
% imag part when multiplying x.*conj(x)
in1 = in(nnn).objs(1).data.getY;
in2 = in(nnn).objs(2).data.getY;
out1 = out(nnn).objs(1).data.getY;
out2 = out(nnn).objs(2).data.getY;
% matrix index convention: H(1,1)->h(1) H(2,1)->h(2) H(1,2)->h(3) H(2,2)->h(4)
v1v1 = conj(out1 - h1.*in1 - h3.*in2).*(out1 - h1.*in1 - h3.*in2);
v2v2 = conj(out2 - h2.*in1 - h4.*in2).*(out2 - h2.*in1 - h4.*in2);
v1v2 = conj(out1 - h1.*in1 - h3.*in2).*(out2 - h2.*in1 - h4.*in2);
v2v1 = conj(out2 - h2.*in1 - h4.*in2).*(out1 - h1.*in1 - h3.*in2);
tmplt1 = h1.*in1 + h3.*in2;
tmplt2 = h2.*in1 + h4.*in2;
%computing SNR
snrexp = utils.math.stnr(tmplt1,tmplt2,out1,out2,InvS11,InvS22,InvS12,InvS21);
snr = snr + 20*log10(snrexp);
log1exp = sum(InvS11.*v1v1 + InvS22.*v2v2 - InvS12.*v1v2 - InvS21.*v2v1);
loglk = loglk + log1exp;
else
error('This method is only implemented for 1 input / 1 output model or for 2 inputs / 2 outputs models');
end
end
end
end